论文标题
不要解析，生成！面向任务的语义解析的序列体系结构的序列
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
论文作者
论文摘要
Amazon Alexa，Apple Siri和Google Assistant等虚拟助手通常会依靠语义解析组件来了解其用户说的话语要执行哪些动作。传统上，基于规则或统计的插槽填充系统已被用来解析“简单”查询。也就是说，包含单个动作并可以分解为一组非重叠实体的查询。最近，已经提出了换档解析器来处理更复杂的话语。这些方法虽然强大，但对可以解析的查询类型施加了特定的限制；也就是说，它们需要查询作为解析树代表。 在这项工作中，我们提出了一个基于序列的统一体系结构，以处理序列模型和指针生成器网络，以处理简单和复杂的查询。与其他作品不同，我们的方法不会对语义解析模式施加任何限制。此外，实验表明，它可以在三个公开可用的数据集（ATIS，SNIPS，Facebook Top）上实现最先进的性能，相对于以前的系统的精确匹配，相对提高了3.3％至7.7％。最后，我们在两个内部数据集上显示了方法的有效性。
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users. Traditionally, rule-based or statistical slot-filling systems have been used to parse "simple" queries; that is, queries that contain a single action and can be decomposed into a set of non-overlapping entities. More recently, shift-reduce parsers have been proposed to process more complex utterances. These methods, while powerful, impose specific limitations on the type of queries that can be parsed; namely, they require a query to be representable as a parse tree. In this work, we propose a unified architecture based on Sequence to Sequence models and Pointer Generator Network to handle both simple and complex queries. Unlike other works, our approach does not impose any restriction on the semantic parse schema. Furthermore, experiments show that it achieves state of the art performance on three publicly available datasets (ATIS, SNIPS, Facebook TOP), relatively improving between 3.3% and 7.7% in exact match accuracy over previous systems. Finally, we show the effectiveness of our approach on two internal datasets.
